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Remote Sensing
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Advanced Lidar Remote Sensing for Atmosphere, Vegetation, and Ocean Observations
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Advanced Lidar Remote Sensing for Atmosphere, Vegetation, and Ocean Observations

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: 30 January 2026 | Viewed by 5151

Special Issue Editors

Dr. Boming Liu

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 4730079, China
Interests: atmospheric aerosol–PBL–cloud interaction
Special Issues, Collections and Topics in MDPI journals
Dr. Zhiyu Zhang

E-Mail Website
Guest Editor
College of Marine Technology, Faculty of Information and Engineering, Ocean University of China, Qingdao 266100, China
Interests: lidar remote sensing; single-photon detection; space-borne lidar modeling and data processing
Dr. Hui Zhou

E-Mail Website
Guest Editor
School of Electronic Information, Wuhan University, Wuhan, China
Interests: data processing and analysis of satellite lidar; vegetation inventory with satellite lidar
Dr. Xin Ma

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 4730079, China
Interests: lidar remote sensing; oceanic lidar; lidar system design
Special Issues, Collections and Topics in MDPI journals
Dr. Yue Ma

E-Mail Website
Guest Editor
School of Electronic Information, Wuhan University, Wuhan 430072, China
Interests: lidar signal modeling and system simulation; signal processing and calibration/validation; coastal applications for satellite laser altimetry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is a great pleasure to present this Special Issue, titled “Advanced Lidar Remote Sensing for Atmosphere, Vegetation, and Ocean Observations”, of the journal Remote Sensing.

As an advanced technology, lidar has attracted increasing interest as a topic of research in recent years. With the development of lidar hardware, the science and technology of lidar remote sensing is rapidly developing, with substantial output of meaningful scientific achievements. Given the wide applications of lidar remote sensing in the fields of atmosphere, vegetation, ocean, and surveying and mapping, the development of lidar remote sensing has ushered in great opportunities and challenges. As a result, the main goal of this Special Issue is to summarize achievements in the development of lidar remote sensing especially satellite lidars, discuss and look forward to the future directions of development, and promote the rapid applications of lidar remote sensing in different fields.

This Special Issue is particularly encouraging submissions on the methods and applications of advanced lidar remote sensing for retrieving atmosphere parameters, monitoring environmental quality, estimating vegetation status from leaf and canopy, obtaining ocean dynamics and even bathymetry, etc. Related advanced hardware designs for ground-based, airborne, and space-based lidar sensors are also of interest, including but not limited to the photon-counting lidar (PCL), high-spectral-resolution lidar (HSRL), etc. In summary, this Special Issue invites submissions exploring cutting-edge research and recent advances in the fields of lidar remote sensing. Both theoretical and experimental studies are welcome, as are comprehensive review and survey papers.

Dr. Boming Liu
Dr. Zhiyu Zhang
Dr. Hui Zhou
Dr. Xin Ma
Dr. Yue Ma
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • lidar remote sensing
  • aerosol and clouds
  • boundary layer
  • leaf and canopy
  • lidar bathymetry
  • photon-counting lidar
  • high-spectral-resolution lidar

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Published Papers (6 papers)

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20 pages, 10558 KiB  
Article
Spatial–Spectral Feature Fusion and Spectral Reconstruction of Multispectral LiDAR Point Clouds by Attention Mechanism
by Guoqing Zhou, Haoxin Qi, Shuo Shi, Sifu Bi, Xingtao Tang and Wei Gong
Remote Sens. 2025, 17(14), 2411; https://doi.org/10.3390/rs17142411 - 12 Jul 2025
Viewed by 334
Abstract
High-quality multispectral LiDAR (MSL) data are crucial for land cover (LC) classification. However, the Titan MSL system encounters challenges of inconsistent spatial–spectral information due to its unique scanning and data saving method, restricting subsequent classification accuracy. Existing spectral reconstruction methods often require empirical [...] Read more.
High-quality multispectral LiDAR (MSL) data are crucial for land cover (LC) classification. However, the Titan MSL system encounters challenges of inconsistent spatial–spectral information due to its unique scanning and data saving method, restricting subsequent classification accuracy. Existing spectral reconstruction methods often require empirical parameter settings and involve high computational costs, limiting automation and complicating application. To address this problem, we introduce the dual attention spectral optimization reconstruction network (DossaNet), leveraging an attention mechanism and spatial–spectral information. DossaNet can adaptively adjust weight parameters, streamline the multispectral point cloud acquisition process, and integrate it into classification models end-to-end. The experimental results show the following: (1) DossaNet exhibits excellent generalizability, effectively recovering accurate LC spectra and improving classification accuracy. Metrics across the six classification models show some improvements. (2) Compared with the method lacking spectral reconstruction, DossaNet can improve the overall accuracy (OA) and average accuracy (AA) of PointNet++ and RandLA-Net by a maximum of 4.8%, 4.47%, 5.93%, and 2.32%. Compared with the inverse distance weighted (IDW) and k-nearest neighbor (KNN) approach, DossaNet can improve the OA and AA of PointNet++ and DGCNN by a maximum of 1.33%, 2.32%, 0.86%, and 2.08% (IDW) and 1.73%, 3.58%, 0.28%, and 2.93% (KNN). The findings further validate the effectiveness of our proposed method. This method provides a more efficient and simplified approach to enhancing the quality of multispectral point cloud data. Full article
(This article belongs to the Special Issue Advanced Lidar Remote Sensing for Atmosphere, Vegetation, and Ocean Observations)
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24 pages, 30364 KiB  
Article
Bayesian Denoising Algorithm for Low SNR Photon-Counting Lidar Data via Probabilistic Parameter Optimization Based on Signal and Noise Distribution
by Qi Liu, Jian Yang, Yue Ma, Wenbo Yu, Qijin Han, Zhibiao Zhou and Song Li
Remote Sens. 2025, 17(13), 2182; https://doi.org/10.3390/rs17132182 - 25 Jun 2025
Viewed by 271
Abstract
The Ice, Cloud, and land Elevation Satellite-2 has provided unprecedented global surface elevation measurements through photon-counting Lidar (Light detection and ranging), yet its low signal-to-noise ratio (SNR) poses significant challenges for denoising algorithms. Existing methods, relying on fixed parameters, struggle to adapt to [...] Read more.
The Ice, Cloud, and land Elevation Satellite-2 has provided unprecedented global surface elevation measurements through photon-counting Lidar (Light detection and ranging), yet its low signal-to-noise ratio (SNR) poses significant challenges for denoising algorithms. Existing methods, relying on fixed parameters, struggle to adapt to dynamic noise distribution in rugged mountain regions where signal and noise change rapidly. This study proposes an adaptive Bayesian denoising algorithm integrating minimum spanning tree (MST) -based slope estimation and probabilistic parameter optimization. First, a simulation framework based on ATL03 data generates point clouds with ground truth labels under varying SNRs, achieving correlation coefficients > 0.9 between simulated and measured distributions. The algorithm then extracts surface profiles via MST and coarse filtering, fits slopes with >0.9 correlation to reference data, and derives the probability distribution function (PDF) of neighborhood photon counts. Bayesian estimation dynamically selects optimal clustering parameters (search radius and threshold), achieving F-scores > 0.9 even at extremely low SNR (1 photon/10 MHz noise). Validation against three benchmark algorithms (OPTICS, quadtree, DRAGANN) on simulated and ATL03 datasets demonstrates superior performance in mountainous terrain, with precision and recall improvements of 10–20% under high noise conditions. This work provides a robust framework for adaptive parameter selection in low-SNR photon-counting Lidar applications. Full article
(This article belongs to the Special Issue Advanced Lidar Remote Sensing for Atmosphere, Vegetation, and Ocean Observations)
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18 pages, 3381 KiB  
Article
Sea Breeze-Driven Variations in Planetary Boundary Layer Height over Barrow: Insights from Meteorological and Lidar Observations
by Hui Li, Wei Gong, Boming Liu, Yingying Ma, Shikuan Jin, Weiyan Wang, Ruonan Fan, Shuailong Jiang, Yujie Wang and Zhe Tong
Remote Sens. 2025, 17(9), 1633; https://doi.org/10.3390/rs17091633 - 5 May 2025
Viewed by 616
Abstract
The planetary boundary layer height (PBLH) in coastal Arctic regions is influenced by sea breeze circulation. However, the specific mechanisms through which sea breeze affects PBLH evolution remain insufficiently explored. This study uses meteorological data, micro-pulse lidar (MPL) data, and sounding profiles from [...] Read more.
The planetary boundary layer height (PBLH) in coastal Arctic regions is influenced by sea breeze circulation. However, the specific mechanisms through which sea breeze affects PBLH evolution remain insufficiently explored. This study uses meteorological data, micro-pulse lidar (MPL) data, and sounding profiles from 2014 to 2021 to investigate the annual and polar day PBLH evolution driven by sea breezes in the Barrow region of Alaska, as well as the specific mechanisms. The results show that sea breeze events significantly suppress PBLH, especially during the polar day, when prolonged solar radiation intensifies the thermal contrast between land and ocean. The cold, moist sea breeze stabilizes the atmospheric conditions, reducing net radiation and sensible heat flux. All these factors inhibit turbulent mixing and PBLH development. Lidar and sounding analyses further reveal that PBLH is lower during sea breeze events compared to non-sea-breeze conditions, with the peak of its probability density distribution occurring at a lower PBLH range. The variable importance in projection (VIP) analysis identifies relative humidity (VIP = 1.95) and temperature (VIP = 1.1) as the primary factors controlling PBLH, highlighting the influence of atmospheric stability in regulating PBLH. These findings emphasize the crucial role of sea breeze in modulating PBL dynamics in the Arctic, with significant implications for improving climate models and studies on pollutant dispersion in polar regions. Full article
(This article belongs to the Special Issue Advanced Lidar Remote Sensing for Atmosphere, Vegetation, and Ocean Observations)
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15 pages, 8483 KiB  
Article
Investigating Wind Characteristics and Temporal Variations in the Lower Troposphere over the Northeastern Qinghai–Tibet Plateau Using a Doppler LiDAR
by Jiafeng Zheng, Yihua Liu, Tingwei Peng, Xia Wan, Xuan Huang, Yuqi Wang, Yuzhang Che and Dongbei Xu
Remote Sens. 2024, 16(11), 1840; https://doi.org/10.3390/rs16111840 - 22 May 2024
Cited by 2 | Viewed by 1396
Abstract
Knowledge of wind field characteristics and variation principles in complex topographical regions is of great importance for the development of numerical prediction models, aviation safety support, and wind energy utilization. However, there has been limited research focused on the lower-tropospheric wind fields in [...] Read more.
Knowledge of wind field characteristics and variation principles in complex topographical regions is of great importance for the development of numerical prediction models, aviation safety support, and wind energy utilization. However, there has been limited research focused on the lower-tropospheric wind fields in the Qinghai-Tibet Plateau. This paper aims to study the wind characteristics, vertical distributions, and temporal variations in the northeast of the plateau by analyzing a four-year continuous dataset collected from a Doppler wind LiDAR deployed in Xining, Qinghai Province of China. The results indicate that the prevailing horizontal wind direction in the low levels is primarily influenced by the mountain-valley wind circulation. However, as the altitude increases, the prevailing winds are predominantly affected by the westerlies. From a diurnal perspective, noticeable transition processes between up-valley and down-valley winds can be observed. The west-northwest wind (down-valley wind) dominates from late night to morning, while the east-southeast wind (up-valley wind) prevails from afternoon to early evening. The vertical winds in the low levels exhibit a downward motion during the daytime and an upward motion during the nighttime. In this plateau valley, the wind shear exponent is found to be highest in spring and lowest in winter, and it is generally lower during the daytime compared to the nighttime. Full article
(This article belongs to the Special Issue Advanced Lidar Remote Sensing for Atmosphere, Vegetation, and Ocean Observations)
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16 pages, 24903 KiB  
Technical Note
A Shipborne Doppler Lidar Investigation of the Winter Marine Atmospheric Boundary Layer over Southeastern China’s Coastal Waters
by Xiaoquan Song, Wenchao Lian, Fuyou Wang, Ping Jiang and Jie Wang
Remote Sens. 2025, 17(13), 2161; https://doi.org/10.3390/rs17132161 - 24 Jun 2025
Viewed by 341
Abstract
The Marine Atmospheric Boundary Layer (MABL), as a critical component of Earth’s climate system, governs the exchange of matter and energy between the ocean surface and the lower atmosphere. This study presents shipborne Doppler lidar observations conducted during 12 January to 3 February [...] Read more.
The Marine Atmospheric Boundary Layer (MABL), as a critical component of Earth’s climate system, governs the exchange of matter and energy between the ocean surface and the lower atmosphere. This study presents shipborne Doppler lidar observations conducted during 12 January to 3 February 2024, along the southeastern Chinese coast. Employing a Coherent Doppler Wind Lidar (CDWL) system onboard the R/V “Yuezhanyu” research vessel, we investigated the spatiotemporal variability of MABL characteristics through integration with ERA5 reanalysis data. The key findings reveal a significant positive correlation between MABL height and surface sensible heat flux in winter, underscoring the dominant role of sensible heat flux in boundary layer development. Through the Empirical Orthogonal Function (EOF) analysis of the ERA5 regional boundary layer height, sensible heat flux, and sea level pressure, we demonstrate MABL height over the coastal seas typically exceeds the corresponding terrestrial atmospheric boundary layer height and exhibits weak diurnal variation. The CDWL observations highlight complex wind field dynamics influenced by synoptic conditions and maritime zones. Compared to onshore regions, the MABL over offshore areas further away from land has lower wind shear changes and a more uniform wind field. Notably, the terrain of Taiwan, China, induces significant low-level jet formations within the MABL. Low-level jets and low boundary layer height promote the pollution episode observed by CDWL. This research provides new insights into MABL dynamics over East Asian marginal seas, with implications for improving boundary layer parameterization in regional climate models and advancing our understanding of coastal meteorological processes. Full article
(This article belongs to the Special Issue Advanced Lidar Remote Sensing for Atmosphere, Vegetation, and Ocean Observations)
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16 pages, 9254 KiB  
Technical Note
Measurement Accuracy and Attitude Compensation of Rayleigh Lidar on an Airborne Floating Platform
by Tong Wu, Kai Zhong, Xianzhong Zhang, Fangjie Li, Xinqi Li, Xiaojian Zhang, Zhaoai Yan, Degang Xu and Jianquan Yao
Remote Sens. 2024, 16(17), 3308; https://doi.org/10.3390/rs16173308 - 5 Sep 2024
Viewed by 1198
Abstract
Rayleigh lidar equipped on airborne floating platforms has received increasing attention in recent years due to the demand for exploring the middle atmosphere. However, the inevitable attitude fluctuation of the platform affects the measurement accuracy of the photon profile, which greatly affects temperature [...] Read more.
Rayleigh lidar equipped on airborne floating platforms has received increasing attention in recent years due to the demand for exploring the middle atmosphere. However, the inevitable attitude fluctuation of the platform affects the measurement accuracy of the photon profile, which greatly affects temperature retrieval. Here, an extensive theoretical analysis model of geometrical transformations between the actual altitude and detection distance under attitude fluctuations was constructed by taking pitch, roll, and observation angles into consideration. Based on this model and measured attitude angles, the influence of platform fluctuation on lidar measurement was analyzed by calculating the deviations between temperature retrieval results and the NRLMSISE-00 model at different observation angles, which demonstrated that the altitude displacement from the variation of pitch angle is a crucial factor in causing temperature retrieval error, especially at large observation angles. Then, an attitude compensation method was designed to eliminate the impact of fluctuations, incorporating the merits of good robustness. Under the observation angle of 45° and average pitch angle of around 4°, the maximum temperature deviation after attitude compensation was reduced from 21.29 K to 0.366 K, a reduction of around two orders of magnitude, indicating that the method can significantly improve the measurement accuracy of Rayleigh lidar. Full article
(This article belongs to the Special Issue Advanced Lidar Remote Sensing for Atmosphere, Vegetation, and Ocean Observations)
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